# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import numpy as np
from op_test import (
    OpTest,
    convert_float_to_uint16,
    get_device_place,
    is_custom_device,
)

import paddle
from paddle import base
from paddle.base import core


def meshgrid_wrapper(x):
    return paddle.tensor.meshgrid(x[0], x[1])


class TestMeshgridOp(OpTest):
    def setUp(self):
        self.op_type = "meshgrid"
        self.prim_op_type = "comp"
        self.python_api = meshgrid_wrapper
        self.public_python_api = meshgrid_wrapper
        self.init_data_type()
        self.init_inputs_and_outputs()
        self.python_out_sig = ['out0', 'out1']
        self.if_enable_cinn()

    def init_data_type(self):
        self.dtype = np.float64

    def test_check_output(self):
        if self.dtype == np.complex64 or self.dtype == np.complex128:
            self.check_output(check_pir=True)
        else:
            self.check_output(
                check_prim=True, check_pir=True, check_prim_pir=True
            )

    def test_check_grad(self):
        if self.dtype == np.complex64 or self.dtype == np.complex128:
            self.check_grad(
                ['x0'],
                ['out0', 'out1'],
                check_pir=True,
            )
        else:
            self.check_grad(
                ['x0'],
                ['out0', 'out1'],
                check_prim=True,
                check_pir=True,
                check_prim_pir=True,
            )

    def init_inputs_and_outputs(self):
        self.shape = self.get_x_shape()
        ins = []
        outs = []
        for i in range(len(self.shape)):
            ins.append(np.random.random((self.shape[i],)).astype(self.dtype))

        for i in range(len(self.shape)):
            out_reshape = [1] * len(self.shape)
            out_reshape[i] = self.shape[i]
            out_temp = np.reshape(ins[i], out_reshape)
            outs.append(np.broadcast_to(out_temp, self.shape))
        self.inputs = {'X': [(f'x{i}', ins[i]) for i in range(len(ins))]}
        self.outputs = {'Out': [(f'out{i}', outs[i]) for i in range(len(outs))]}

    def get_x_shape(self):
        return [100, 200]

    def if_enable_cinn(self):
        # 拆解tile_grad导致cinn运行超时
        self.enable_cinn = False


class TestMeshgridOp2(TestMeshgridOp):
    def get_x_shape(self):
        return [100, 300]


class TestMeshgridOp2Fp16(TestMeshgridOp):
    def get_x_shape(self):
        return [100, 300]

    def init_data_type(self):
        self.dtype = np.float16


class TestMeshgridOp2Complex64(TestMeshgridOp):
    def get_x_shape(self):
        return [100, 300]

    def init_data_type(self):
        self.dtype = np.complex64


class TestMeshgridOp2Complex128(TestMeshgridOp):
    def get_x_shape(self):
        return [100, 300]

    def init_data_type(self):
        self.dtype = np.complex128


@unittest.skipIf(
    not (core.is_compiled_with_cuda() or is_custom_device())
    or not core.is_bfloat16_supported(get_device_place()),
    "core is not compiled with CUDA or not support the bfloat16",
)
class TestMeshgridOpBFP16OP(TestMeshgridOp):
    def init_data_type(self):
        self.data_type = np.uint16

    def init_inputs_and_outputs(self):
        self.shape = self.get_x_shape()
        ins = []
        outs = []
        for i in range(len(self.shape)):
            ins.append(np.random.random((self.shape[i],)).astype(self.dtype))

        for i in range(len(self.shape)):
            out_reshape = [1] * len(self.shape)
            out_reshape[i] = self.shape[i]
            out_temp = np.reshape(ins[i], out_reshape)
            outs.append(np.broadcast_to(out_temp, self.shape))
        self.inputs = {
            'X': [
                (f'x{i}', convert_float_to_uint16(ins[i]))
                for i in range(len(ins))
            ]
        }
        self.outputs = {
            'Out': [
                (f'out{i}', convert_float_to_uint16(outs[i]))
                for i in range(len(outs))
            ]
        }

    def if_enable_cinn(self):
        self.enable_cinn = False

    def test_check_output(self):
        self.check_output_with_place(
            place=get_device_place(), check_pir=True, check_prim_pir=True
        )

    def test_check_grad(self):
        self.check_grad_with_place(
            get_device_place(),
            ['x0'],
            ['out0', 'out1'],
            check_prim=True,
            check_pir=True,
            check_prim_pir=True,
        )


class TestMeshgridOp3(unittest.TestCase):
    def test_api(self):
        input_1 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('int32')
        input_2 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('int32')

        out_1 = np.reshape(input_1, [100, 1])
        out_1 = np.broadcast_to(out_1, [100, 200])
        out_2 = np.reshape(input_2, [1, 200])
        out_2 = np.broadcast_to(out_2, [100, 200])

        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(shape=[100], dtype='int32', name='x')
            y = paddle.static.data(shape=[200], dtype='int32', name='y')

            exe = base.Executor(place=base.CPUPlace())
            grid_x, grid_y = paddle.tensor.meshgrid(x, y)
            res_1, res_2 = exe.run(
                paddle.static.default_main_program(),
                feed={'x': input_1, 'y': input_2},
                fetch_list=[grid_x, grid_y],
            )
        np.testing.assert_array_equal(res_1, out_1)
        np.testing.assert_array_equal(res_2, out_2)


class TestMeshgridOp4(unittest.TestCase):
    def test_list_input(self):
        input_1 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('int32')
        input_2 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('int32')

        out_1 = np.reshape(input_1, [100, 1])
        out_1 = np.broadcast_to(out_1, [100, 200])
        out_2 = np.reshape(input_2, [1, 200])
        out_2 = np.broadcast_to(out_2, [100, 200])

        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(shape=[100], dtype='int32', name='x')
            y = paddle.static.data(shape=[200], dtype='int32', name='y')

            exe = base.Executor(place=base.CPUPlace())
            grid_x, grid_y = paddle.tensor.meshgrid([x, y])
            res_1, res_2 = exe.run(
                paddle.static.default_main_program(),
                feed={'x': input_1, 'y': input_2},
                fetch_list=[grid_x, grid_y],
            )
        np.testing.assert_array_equal(res_1, out_1)
        np.testing.assert_array_equal(res_2, out_2)


class TestMeshgridOp5(unittest.TestCase):
    def test_tuple_input(self):
        input_1 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('int32')
        input_2 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('int32')

        out_1 = np.reshape(input_1, [100, 1])
        out_1 = np.broadcast_to(out_1, [100, 200])
        out_2 = np.reshape(input_2, [1, 200])
        out_2 = np.broadcast_to(out_2, [100, 200])

        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(shape=[100], dtype='int32', name='x')
            y = paddle.static.data(shape=[200], dtype='int32', name='y')

            exe = base.Executor(place=base.CPUPlace())
            grid_x, grid_y = paddle.tensor.meshgrid((x, y))
            res_1, res_2 = exe.run(
                paddle.static.default_main_program(),
                feed={'x': input_1, 'y': input_2},
                fetch_list=[grid_x, grid_y],
            )
        np.testing.assert_array_equal(res_1, out_1)
        np.testing.assert_array_equal(res_2, out_2)


class TestMeshgridOp6(unittest.TestCase):
    def test_api_with_dygraph(self):
        input_3 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('int32')
        input_4 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('int32')

        with base.dygraph.guard():
            tensor_3 = paddle.to_tensor(input_3)
            tensor_4 = paddle.to_tensor(input_4)
            res_3, res_4 = paddle.tensor.meshgrid(tensor_3, tensor_4)

            np.testing.assert_array_equal(res_3.shape, [100, 200])
            np.testing.assert_array_equal(res_4.shape, [100, 200])


class TestMeshgridOpIndexing(unittest.TestCase):
    def setUp(self):
        self.input_3 = np.random.randint(0, 100, [100]).astype('int32')
        self.input_4 = np.random.randint(0, 100, [200]).astype('int32')

    def test_api_with_dygraph_indexing_xy(self):
        np_res_3, np_res_4 = np.meshgrid(
            self.input_3, self.input_4, indexing='xy'
        )

        with base.dygraph.guard():
            tensor_3 = paddle.to_tensor(self.input_3)
            tensor_4 = paddle.to_tensor(self.input_4)
            res_3, res_4 = paddle.tensor.meshgrid(
                tensor_3, tensor_4, indexing='xy'
            )

            np.testing.assert_array_equal(res_3.shape, np_res_3.shape)
            np.testing.assert_array_equal(res_4.shape, np_res_4.shape)
            np.testing.assert_array_equal(res_3.numpy(), np_res_3)
            np.testing.assert_array_equal(res_3.numpy(), np_res_3)
            np.testing.assert_array_equal(res_4.numpy(), np_res_4)

    def test_api_with_dygraph_indexing_ij(self):
        np_res_3, np_res_4 = np.meshgrid(
            self.input_3, self.input_4, indexing='ij'
        )

        with base.dygraph.guard():
            tensor_3 = paddle.to_tensor(self.input_3)
            tensor_4 = paddle.to_tensor(self.input_4)
            res_3, res_4 = paddle.tensor.meshgrid(
                tensor_3, tensor_4, indexing='ij'
            )

            np.testing.assert_array_equal(res_3.shape, np_res_3.shape)
            np.testing.assert_array_equal(res_4.shape, np_res_4.shape)
            np.testing.assert_array_equal(res_3.numpy(), np_res_3)
            np.testing.assert_array_equal(res_4.numpy(), np_res_4)

    def test_indexing_default(self):
        np_res_3, np_res_4 = np.meshgrid(
            self.input_3, self.input_4, indexing='ij'
        )

        with base.dygraph.guard():
            tensor_3 = paddle.to_tensor(self.input_3)
            tensor_4 = paddle.to_tensor(self.input_4)
            res_3, res_4 = paddle.tensor.meshgrid(tensor_3, tensor_4)
            res_3_n, res_4_n = paddle.tensor.meshgrid(
                tensor_3, tensor_4, indexing=None
            )
            np.testing.assert_array_equal(res_3.numpy(), np_res_3)
            np.testing.assert_array_equal(res_4.numpy(), np_res_4)
            np.testing.assert_array_equal(res_3_n.numpy(), np_res_3)
            np.testing.assert_array_equal(res_4_n.numpy(), np_res_4)

    def test_indexing_invalid_value(self):
        with base.dygraph.guard():
            tensor_3 = paddle.to_tensor(self.input_3)
            tensor_4 = paddle.to_tensor(self.input_4)
            invalid_indexing = "ab"
            with self.assertRaises(ValueError) as cm:
                res_3, res_4 = paddle.tensor.meshgrid(
                    tensor_3, tensor_4, indexing=invalid_indexing
                )


class TestMeshgridOp7(unittest.TestCase):
    def test_api_with_dygraph_list_input(self):
        input_3 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('int32')
        input_4 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('int32')

        with base.dygraph.guard():
            tensor_3 = paddle.to_tensor(input_3)
            tensor_4 = paddle.to_tensor(input_4)
            res_3, res_4 = paddle.tensor.meshgrid([tensor_3, tensor_4])

            np.testing.assert_array_equal(res_3.shape, [100, 200])
            np.testing.assert_array_equal(res_4.shape, [100, 200])


class TestMeshgridOp8(unittest.TestCase):
    def test_api_with_dygraph_tuple_input(self):
        input_3 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('int32')
        input_4 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('int32')

        with base.dygraph.guard():
            tensor_3 = paddle.to_tensor(input_3)
            tensor_4 = paddle.to_tensor(input_4)
            res_3, res_4 = paddle.tensor.meshgrid((tensor_3, tensor_4))

            np.testing.assert_array_equal(res_3.shape, [100, 200])
            np.testing.assert_array_equal(res_4.shape, [100, 200])


class TestMeshgridOpComplexStatic(unittest.TestCase):
    def test_tuple_input(self):
        input_1 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('complex64')
        input_2 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('complex64')

        out_1 = np.reshape(input_1, [100, 1])
        out_1 = np.broadcast_to(out_1, [100, 200])
        out_2 = np.reshape(input_2, [1, 200])
        out_2 = np.broadcast_to(out_2, [100, 200])

        with paddle.static.program_guard(paddle.static.Program()):
            x = paddle.static.data(shape=[100], dtype='complex64', name='x')
            y = paddle.static.data(shape=[200], dtype='complex64', name='y')

            exe = base.Executor(place=base.CPUPlace())
            grid_x, grid_y = paddle.tensor.meshgrid((x, y))
            res_1, res_2 = exe.run(
                paddle.static.default_main_program(),
                feed={'x': input_1, 'y': input_2},
                fetch_list=[grid_x, grid_y],
            )
        np.testing.assert_array_equal(res_1, out_1)
        np.testing.assert_array_equal(res_2, out_2)


class TestMeshgridOpComplexDygraph(unittest.TestCase):
    def test_api_with_dygraph_tuple_input(self):
        input_3 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('complex64')
        input_4 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('complex64')

        with base.dygraph.guard():
            tensor_3 = paddle.to_tensor(input_3)
            tensor_4 = paddle.to_tensor(input_4)
            res_3, res_4 = paddle.tensor.meshgrid((tensor_3, tensor_4))

            np.testing.assert_array_equal(res_3.shape, [100, 200])
            np.testing.assert_array_equal(res_4.shape, [100, 200])


class TestMeshGrid_ZeroDim(TestMeshgridOp):
    def init_inputs_and_outputs(self):
        self.shape = self.get_x_shape()
        ins = []
        outs = []
        ins.append(np.random.random([]).astype(self.dtype))
        ins.append(np.random.random([2]).astype(self.dtype))
        ins.append(np.random.random([3]).astype(self.dtype))
        for i in range(len(self.shape)):
            out_reshape = [1] * len(self.shape)
            out_reshape[i] = self.shape[i]
            out_temp = np.reshape(ins[i], out_reshape)
            outs.append(np.broadcast_to(out_temp, self.shape))
        self.inputs = {'X': [(f'x{i}', ins[i]) for i in range(len(ins))]}
        self.outputs = {'Out': [(f'out{i}', outs[i]) for i in range(len(outs))]}

    def get_x_shape(self):
        return [1, 2, 3]

    def if_enable_cinn(self):
        self.enable_cinn = False


class TestMeshgridEager(unittest.TestCase):
    def test_dygraph_api(self):
        input_1 = np.random.randint(
            0,
            100,
            [
                100,
            ],
        ).astype('int32')
        input_2 = np.random.randint(
            0,
            100,
            [
                200,
            ],
        ).astype('int32')

        with base.dygraph.guard():
            tensor_1 = paddle.to_tensor(input_1)
            tensor_2 = paddle.to_tensor(input_2)
            tensor_1.stop_gradient = False
            tensor_2.stop_gradient = False
            res_1, res_2 = paddle.tensor.meshgrid((tensor_1, tensor_2))
            sum = paddle.add_n([res_1, res_2])
            sum.backward()
            tensor_eager_1 = paddle.to_tensor(input_1)
            tensor_eager_2 = paddle.to_tensor(input_2)
            tensor_eager_1.stop_gradient = False
            tensor_eager_2.stop_gradient = False
            res_eager_1, res_eager_2 = paddle.tensor.meshgrid(
                (tensor_eager_1, tensor_eager_2)
            )
            sum_eager = paddle.add_n([res_eager_1, res_eager_2])
            sum_eager.backward()
            self.assertEqual(
                (tensor_1.grad.numpy() == tensor_eager_1.grad.numpy()).all(),
                True,
            )
            self.assertEqual(
                (tensor_2.grad.numpy() == tensor_eager_2.grad.numpy()).all(),
                True,
            )


class TestMeshgridEmptyTensor(unittest.TestCase):
    def _get_places(self):
        places = [base.CPUPlace()]
        if paddle.is_compiled_with_cuda() or is_custom_device():
            places.append(get_device_place())
        return places

    def _generate_inputs(self, shapes):
        return [np.random.random(shape).astype('float64') for shape in shapes]

    def _test_with_shapes(self, shapes, expected_shapes, place=None):
        inputs = self._generate_inputs(shapes)

        if place is None:  # Dygraph mode
            with base.dygraph.guard():
                tensors = [paddle.to_tensor(inp) for inp in inputs]
                results = paddle.meshgrid(tensors)
        else:  # Static mode
            with paddle.static.program_guard(paddle.static.Program()):
                data_tensors = [
                    paddle.static.data(
                        shape=shape, dtype='float64', name=f'x{i}'
                    )
                    for i, shape in enumerate(shapes)
                ]
                exe = base.Executor(place=place)
                grid_results = paddle.tensor.meshgrid(data_tensors)
                feed_dict = {f'x{i}': inp for i, inp in enumerate(inputs)}
                results = exe.run(
                    paddle.static.default_main_program(),
                    feed=feed_dict,
                    fetch_list=grid_results,
                )

        for result, expected_shape in zip(results, expected_shapes):
            np.testing.assert_array_equal(result.shape, expected_shape)

    def test_api_with_dygraph_empty_tensor_input(self):
        self._test_with_shapes([(100,), (0,)], [[100, 0], [100, 0]])

    def _test_api_with_static_empty_tensor_input(self, place):
        self._test_with_shapes([(100,), (0,)], [[100, 0], [100, 0]], place)

    def test_api_with_static_empty_tensor_input(self):
        for place in self._get_places():
            self._test_api_with_static_empty_tensor_input(place)


class TestMeshgridEmptyTensor2(TestMeshgridEmptyTensor):
    def test_api_with_dygraph_empty_tensor_input(self):
        self._test_with_shapes(
            [(0,), (0,), (0,)], [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
        )

    def _test_api_with_static_empty_tensor_input(self, place):
        self._test_with_shapes(
            [(0,), (0,), (0,)], [[0, 0, 0], [0, 0, 0], [0, 0, 0]], place
        )


class TestMeshgridZeroSizeGrad(unittest.TestCase):
    def test_zero_size_grad_dynamic(self):
        with base.dygraph.guard():
            x = paddle.to_tensor(
                np.ones([3]), dtype="float32", stop_gradient=False
            )
            y = paddle.to_tensor(
                np.ones([0]), dtype="float32", stop_gradient=False
            )
            x_grid, y_grid = paddle.meshgrid(x, y)
            z = paddle.sum(x_grid + y_grid)
            z.backward()
            x_grad_expacted = np.zeros([3])
            y_grad_expacted = np.zeros([0])
            np.testing.assert_array_equal(x.grad, x_grad_expacted)
            np.testing.assert_array_equal(y.grad, y_grad_expacted)


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()
